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Automated cloud-based workflow for quantification of MRI signal intensity - initial real-world clinical validation

Authors:

Abstract

One-third of brain MRI scans performed worldwide make use of gadolinium-based contrast agent injections to enable detection of the breakdown in the blood-brain barrier by the resulting enhancement. Observations have shown increased signal intensity, particularly in the globus pallidus and thalamus after multiple doses of linear contrast agents but there is no standard procedure to measure this contrast intensity. Current quantitative methods are manual, labor intensive, time-consuming and provide variable results. We present a fully automatic workflow which accelerates the investigation of signal intensity in these nuclei after multiple doses of contrast agents by extracting the T1-weighted modal intensity value and applying appropriate corrections and normalizations to allow comparison across acquisitions and protocols. Automatic results matched up to 94% correlation with manual results and reduced the time by 90%.
Automated cloud-based workflow for
quantification of MRI signal intensity:
initial real-world clinical validation
Marc Ramos 1, Vesna Prčkovska 1, Paulo Rodrigues 1, Jinnan Wang 2,
Franklin Moser 3, Markus Blank 2, Sheela Agarwal 2, Jacob Agris 2,
David Moreno-Dominguez 1
1 QMENTA Inc, 2 Bayer Radiology, 3 Cedars Sinai Medical Center
2019 - Marc Ramos - QMENTA - marc@qmenta.com 2
Speaker Name: David Moreno-Dominguez
I have the following financial interest or relationship to disclose with
regard to the subject matter of this presentation:
Company Name: QMENTA
Type of Relationship: Employee and stock options holder
Declaration of
Financial Interests or Relationships
2019 - Marc Ramos - QMENTA - marc@qmenta.com
Introduction
Gadolinium contrast
Gadolinium (Gd) based contrast
agents (GBCA) have been widely
used in clinical MRI for the last 30
years.
While GBCA has been considered
safe, Gd is highly toxic.
GBCA has been associated with
incidence of nephrogenic systemic
fibrosis, and recently, with
elevated signal intensities in
unenhanced T1-weighted images.
Standard intensity
measurement
Current standard procedure
consists of manual delineation of
brain regions of interest and
averaging of the signal intensity
(SI) within the regions.
It is labor-intensive,
time-consuming and susceptible
to rater bias.
Our approach
In this work we have developed a fully automated pipeline to reduce time and increase reproducibility,
and validated it against manual results 3
2019 - Marc Ramos - QMENTA - marc@qmenta.com
Data
Patients with at least eight sessions of GBCA-enhanced MRI scan.
Linear contrasts: Gadoversetamide, Gadobenate dimeglumine, Gadodiamide.
Macrocyclic contrasts: Gadobutrol.
Injected with only linear or macrocyclic.
113 patients with a total of 205 MRI sessions1
T1-weighted images from 1.5T and 3T Siemens scanners at Cedars-Sinai Medical Center.
1.5T: TR 1330 ms; TE 4.8 ms; TI 800 ms; flip angle 15°; section thickness 12.5 mm; matrix size: 256 × 192; echo-train length 1.
3T: TR 2100 ms; TE 3.0 ms; TI 900 ms; flip angle 9°; section thickness 11 mm; matrix size 256 × 256; echo train length 1.
1. Wang et al. (2018). Automated signal intensity quantification software:
initial “real world” clinical validation. In Western Neuroradiological Society 49th Annual Meeting.
DOI: 10.13140/RG.2.2.18057.90727
4
2019 - Marc Ramos - QMENTA - marc@qmenta.com
Methods: Automatic Pipeline
1. Detect the most recent session (MRS) of patient with valid T1w image.
2. Atlas-based segmentation of ROIS in MRS image:
Globus Pallidus, Thalamus, Dentate Nucleus, Pons
3. Non-linear registration across timepoints using ANTs (to accommodate
region displacement due to deformations), warp MRS regions to all
longitudinal sessions.
4. Extract the SI modal values using maximum of Gaussian kernel-density
estimates (KDEs) curve.
5. Correction of SI values for differing sequence parameters using signal
equations and tissue constant values.
6. SI normalization using reference ROIs:
Pallidus-Thalamus; Dentate-Pons
5
2019 - Marc Ramos - QMENTA - marc@qmenta.com
4. SI KDE and
modal value
estimation
1. Last
Session
selection
Thalamus
Globus
Pallidus
DentatePons
3. Non linear warping T1-w regions
2. Automatic
Segmentation
and SI Analysis
Pipeline
Input data - N sessions
6. SI correction
7. SI normalization
Methods: Automatic Pipeline
6
2019 - Marc Ramos - QMENTA - marc@qmenta.com
SI Correction
Use of MR signal equation for each SI measurement, based on
mag. field strength, sequence type, and tissue type constants 1.
Allows for correction of differing TI, TR and TE parameters within
same sequence type.
Methods: SI Correction and Normalization
7
Equations for correcting by TE, TR and flip angle¹:
Spin-Echo
Inversion Recovery
1. Fletcher, Lynn M., John B. Barsotti, and Joseph P. Hornak. "A multispectral analysis of brain tissues." Magnetic Resonance in Medicine 29.5 (1993): 623-630.
SI Normalization
Globus pallidus values are normalized over thalamus ones.
Dentate nucleus values are normalized over pons ones.
Allows for comparison across scans
2019 - Marc Ramos - QMENTA - marc@qmenta.com
Results: Initial clinical validation
8
Manual processing was performed by two radiologists who delineated the ROIs and recorded average
signal intensity.
Strong correlation was found between manual and automated analysis.
Very high correlation coefficients were found in all regions.
Dentate Nucleus: 0.94, Globus Pallidus: 0.9 and Pons: 0.93.
8
Manual results
Auto results
2019 - Marc Ramos - QMENTA - marc@qmenta.com
Results: Automatic processing of all sessions
9
Session #
Macrocyclic
Linear
2019 - Marc Ramos - QMENTA - marc@qmenta.com
Discussion
Parallel cloud computing allowed the algorithm to process all sessions data in 10 hours, compared to 100 hours it
would take of radiologist time.
Manual results might be a biased gold-standard as the SI measurements during the manual review were made
based on oval ROI while the software segments whole structure.
Future work:
The tool can be easily extended to any other MRI sequence and ROI
Exploration of additional clinically relevant applications of the pipeline
Improve the correlation between manual vs. automatic values
10
Thank you for your
attention
David Moreno-Dominguez
Contact
david@qmenta.com
Booth 1010
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Gadolinium deposition in the brain: summary of evidence and recommendations
  • Dr Vikas Gulani
  • M D Fernando Calamante
  • Phd Frank G Shellock
  • Phd Emanuel Kanal
  • M D Scott
  • B Reeder
  • Md
Dr Vikas Gulani, MD. Prof Fernando Calamante, PhD. Prof Frank G Shellock, PhD. Prof Emanuel Kanal, MD. Prof Scott B Reeder, MD et al. (2017) Gadolinium deposition in the brain: summary of evidence and recommendations DOI: 10.1016/S1474-4422(17)30158-8.